How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code

3 months ago, we started a revival of Papers with Code (see also the announcement tweet). Its goal is to make open AI research accessible and digestible, so that people can easily find the artifacts related to a paper, find state-of-the-art (SOTA) across the various domains of AI, share interesting research and build on top of each other's work. In other words, its goal is to power the wave of research that leads to the next Transformer.

Of course, making AI research accessible requires a powerful search engine, so that humans and agents can quickly find relevant and related work, either through the website or the pwc search CLI command, which agents can use via the Skill.

It's important to note that searching for research is not quite the same as searching for regular text. A useful paper search engine should find an exact title or arXiv identifier, but it should also understand a query such as “small language models for code generation” even when those words do not appear together in a paper. It needs to recognize that “the original BERT paper” is a navigational request, tolerate an incomplete title or typos, and still respond quickly when a model service is cold or temporarily unavailable.

For Papers with Code, we built this as a hybrid search system. This is also based on our prior experience at ML6, where we developed RAG-based systems for clients. It turned out that hybrid search typically outperforms keyword- and vector-based search systems, as it combines the best of both worlds (see also this blog for more info). Keyword search finds exact mentions, whereas vector search finds more fuzzy, semantically similar terms. Note that rerankers (also called cross-encoders) can further improve the results, although they also come with additional overhead and latency.

TL;DR

We deliberately split search into an offline corpus build and an online search service:

Start with a strict embedding contract

normalized title + "\n\n" + normalized abstract

The worker: